{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9df8d58a-0b71-4041-b016-d31a5dfc5074",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.miscmodels.ordinal_model import OrderedModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a7bc2aa5-b9bf-4e29-a625-bc292054fed3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel(r'C\\df_q2_q3.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "334c2035-4cc9-49fb-88d7-ac83ff104ebe",
   "metadata": {},
   "source": [
    "# Figure 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0607b176-3827-44e0-b0cf-38381fb56ba3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'Position', 'Impeachment Support', 'Martial Law Limits Democracy',\n",
       "       'Martial Law Necessary for Security', 'Past Martial Laws Justified',\n",
       "       'Democracy Priority in Crisis', 'Security Justifies Freedom Limits',\n",
       "       'Age', 'Gender', 'Income', 'Education', 'Political Orientation',\n",
       "       'Religion'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "6b5b86ef-36fc-424a-bc46-95f317b46c7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_ologit(df, dv, ivs):\n",
    "    model = OrderedModel(df[dv],\n",
    "                         df[ivs],\n",
    "                         distr='logit')\n",
    "    res = model.fit(method='bfgs', disp=False)\n",
    "    return res\n",
    "\n",
    "ivs = ['Past Martial Laws Justified', 'Age', 'Gender', 'Income', 'Education', 'Religion', 'Position']\n",
    "dv1 = 'Impeachment Support'\n",
    "dv2 = 'Martial Law Limits Democracy'\n",
    "dv3 = 'Martial Law Necessary for Security'\n",
    "\n",
    "res1 = run_ologit(df, dv1, ivs)\n",
    "res2 = run_ologit(df, dv2, ivs)\n",
    "res3 = run_ologit(df, dv3, ivs)\n",
    "\n",
    "def extract_coef_ci(res, var, model_label):\n",
    "    coef = res.params[var]\n",
    "    se = res.bse[var]\n",
    "    # 90% and 95% CI\n",
    "    z_95 = 1.96\n",
    "    z_90 = 1.645\n",
    "    return {\n",
    "        'Model': model_label,\n",
    "        'Coefficient': np.exp(coef),\n",
    "        'Lower_95': np.exp(coef - z_95 * se),\n",
    "        'Upper_95': np.exp(coef + z_95 * se),\n",
    "        'Lower_90': np.exp(coef - z_90 * se),\n",
    "        'Upper_90': np.exp(coef + z_90 * se)\n",
    "    }\n",
    "\n",
    "coefs = pd.DataFrame([\n",
    "    extract_coef_ci(res1, 'Past Martial Laws Justified', '(a) DV: Impeachment'),\n",
    "    extract_coef_ci(res2, 'Past Martial Laws Justified', '(b) DV: Limits Democracy'),\n",
    "    extract_coef_ci(res3, 'Past Martial Laws Justified', '(c) DV: Necessary for Security')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "202d416e-459d-42e7-921c-eb0374e6effc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 5))  # Slightly wider for better label spacing\n",
    "\n",
    "for i, row in coefs.iterrows():\n",
    "    # 95% CI (black)\n",
    "    ax.errorbar(x=row['Coefficient'], y=i, \n",
    "                xerr=[[row['Coefficient'] - row['Lower_95']], \n",
    "                      [row['Upper_95'] - row['Coefficient']]],\n",
    "                fmt='o', color='black', capsize=4, label='95% CI' if i == 0 else \"\")\n",
    "    \n",
    "    # Add odds ratio number right above the dot\n",
    "    ax.text(row['Coefficient'], i - 0.04, f\"{row['Coefficient']:.3f}\",\n",
    "            ha='center', va='bottom', fontsize=12, color='black')\n",
    "\n",
    "# reference line at OR = 1\n",
    "ax.axvline(1, color='red', linestyle='--', linewidth=0.8)\n",
    "\n",
    "# aesthetics\n",
    "ax.set_yticks(range(len(coefs)))\n",
    "ax.set_yticklabels(coefs['Model'], fontsize=12)\n",
    "ax.set_xlabel('Odds Ratio', fontsize=14)\n",
    "ax.set_title('Effect of Past Martial Laws Justified on Each DV', fontsize=16, pad=10)\n",
    "ax.invert_yaxis()\n",
    "ax.set_facecolor('white')\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "plt.legend(fontsize=12)\n",
    "\n",
    "ax.set_ylim(len(coefs) - 0.5, -0.5)\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef0384b6-b503-488e-abad-586204a347d0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "9afa72af-cf5e-4a51-81d9-cc0f1e37af3d",
   "metadata": {},
   "source": [
    "# Figure 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "8e573df8-ba25-44e2-a77b-d1514a801fba",
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_ologit(df, dv, ivs):\n",
    "    model = OrderedModel(df[dv],\n",
    "                         df[ivs],\n",
    "                         distr='logit')\n",
    "    res = model.fit(method='bfgs', disp=False)\n",
    "    return res\n",
    "\n",
    "ivs = ['Political Orientation', 'Age', 'Gender', 'Income', 'Education', 'Religion', 'Position']\n",
    "dv1 = 'Democracy Priority in Crisis'\n",
    "dv2 = 'Security Justifies Freedom Limits'\n",
    "\n",
    "\n",
    "res1 = run_ologit(df, dv1, ivs)\n",
    "res2 = run_ologit(df, dv2, ivs)\n",
    "\n",
    "\n",
    "def extract_coef_ci(res, var, model_label):\n",
    "    coef = res.params[var]\n",
    "    se = res.bse[var]\n",
    "    # 90% and 95% CI\n",
    "    z_95 = 1.96\n",
    "    z_90 = 1.645\n",
    "    return {\n",
    "        'Model': model_label,\n",
    "        'Coefficient': np.exp(coef),\n",
    "        'Lower_95': np.exp(coef - z_95 * se),\n",
    "        'Upper_95': np.exp(coef + z_95 * se),\n",
    "        'Lower_90': np.exp(coef - z_90 * se),\n",
    "        'Upper_90': np.exp(coef + z_90 * se)\n",
    "    }\n",
    "\n",
    "coefs = pd.DataFrame([\n",
    "    extract_coef_ci(res1, 'Political Orientation', '(a) DV: Democracy Priority'),\n",
    "    extract_coef_ci(res2, 'Political Orientation', '(b) DV: Restrictions on Liberties '),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "41961368-f9d2-4ada-bb87-8c8c1daf48bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 5))  # Slightly wider for better label spacing\n",
    "\n",
    "for i, row in coefs.iterrows():\n",
    "    # 95% CI (black)\n",
    "    ax.errorbar(x=row['Coefficient'], y=i, \n",
    "                xerr=[[row['Coefficient'] - row['Lower_95']], \n",
    "                      [row['Upper_95'] - row['Coefficient']]],\n",
    "                fmt='o', color='black', capsize=4, label='95% CI' if i == 0 else \"\")\n",
    "    \n",
    "    # Add odds ratio number right above the dot\n",
    "    ax.text(row['Coefficient'], i - 0.04, f\"{row['Coefficient']:.3f}\",\n",
    "            ha='center', va='bottom', fontsize=12, color='black')\n",
    "\n",
    "# reference line at OR = 1\n",
    "ax.axvline(1, color='red', linestyle='--', linewidth=0.8)\n",
    "\n",
    "# aesthetics\n",
    "ax.set_yticks(range(len(coefs)))\n",
    "ax.set_yticklabels(coefs['Model'], fontsize=12)\n",
    "ax.set_xlabel('Odds Ratio', fontsize=14)\n",
    "ax.set_title('Effect of Political Ideology on Each DV', fontsize=16, pad=10)\n",
    "ax.invert_yaxis()\n",
    "ax.set_facecolor('white')\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "plt.legend(fontsize=12)\n",
    "\n",
    "# Add vertical padding so points are not stuck at top/bottom\n",
    "ax.set_ylim(len(coefs) - 0.5, -0.5)\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ce84a1e-b853-41ce-9658-aad77360ac0c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cef6a98d-d135-4ede-bc5d-84b7b32bf182",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "afb7f43d-a7f9-442f-afa0-3ec01fbe66f4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
